This invention relates generally to processing physiological signals and assessment of the processed signals to detect a health condition. As a specific example, a process and analysis for detecting sleep apnea from heart rate variability signals derived from electrocardiograms (EKG) signals is described.
Obstructive sleep apnea (OSA) is a significant health problem with health implications, ranging from excessive daytime drowsiness to serious cardiac arrhythmias. OSA, or intermittent cessation of breathing, is associated with increased risks of high blood pressure, myocardial infarction, and stroke. Sleep apnea is associated with increased mortality rates.
More particularly, OSA is conventionally defined as the total cessation of breathing during sleep for 10 seconds or more. This cessation is caused by an airway blockage in spite of a continuous respiratory nervous effort. This breath loss leads to a drop in blood oxygenation levels and sleep interruptions. Partial obstruction, Obstructive Sleep Hypopnea (OSH) is defined as the partial cessation (50% or more) of breathing during sleep for 10 seconds or more at a time.
Standard methods for detecting and quantifying sleep apnea and sleep disorders are based on respiration monitoring. Respiration monitoring is expensing and often disturbs or interferes with sleep, compounding the sleep problem. Further, evaluations for sleep breathing disorders, are typically done by specialists which are not accessible to a large portion of the population. The actual prevalence of sleep apnea is elusive.
A number of recent studies have eluded to the possibility of detecting sleep apnea using electrocardiograms (EKGs). EKG recordings are minimally intrusive and relatively inexpensive. Different research groups have concentrated on different features of the EKG overnight recordings. (de Chazel et al., IEEE Transactions on Biomedical Engineering, 2003, 50 (6), pp. 686-696; (Vijendra, Master's Thesis, Dept of Biomedical Engineering, University of Texas at Arlington, Arlington, Tex., USA, 2003; Penzel et al., Medical and Biological Engineering and Computing, 2002 40, pp. 402-407). Some groups concentrated on time-domain characteristics of the EKG signal, such as Angle of Mean Electrical Axis and Heart Rate Variability (HRV). Other groups looked at frequency-domain parameters such as PSD (Vijendra; Penzel et al.) and time-frequency plots (de Chazel et al.; Hilton et al., Medical and Biological Engineering and Computing, 1999, 37, pp. 760-769). However, time-frequency plots have only been studied in a qualitative manner, in which some noted differences between normal subjects and OSA patients have been described but never quantified or assessed for accuracy. Even with these limitations, the studies support the conclusion that HRV signals carry numerous measures that are sensitive to sleep disordered breathing, as well as other disorders influencing the cardiovascular system.
Thus, a method of processing, quantifying, and analyzing HRV signals, derived from EKGs, to detect apneic episodes is desirable. The accuracy of the method also needs to be established for clinical applications and other evaluations.